Stereo depth estimation relies on optimal correspondence matching between
pixels on epipolar lines in the left and right images to infer depth. In this
work, we revisit the problem from a sequence-to-sequence correspondence
perspective to replace cost volume construction with dense pixel matching using
position information and attention. This approach, named STereo TRansformer
(STTR), has several advantages: It 1) relaxes the limitation of a fixed
disparity range, 2) identifies occluded regions and provides confidence
estimates, and 3) imposes uniqueness constraints during the matching process.
We report promising results on both synthetic and real-world datasets and
demonstrate that STTR generalizes across different domains, even without
fine-tuning.

本文介绍了一种名为 STereo TRansformer (STTR) 的方法，采用序列到序列的对应关系角度来进行立体深度估计，使用位置信息和注意力机制来进行密集像素匹配，克服了固定视差范围的限制，识别遮挡区域和提供置信度估计，并在合成和真实数据集上取得了良好的结果。